Evidence map›Paper›PMID 39624411›Full record

ArticleFrontiers in public health2024

Socioecological drivers of burnout - a mixed methods study of military health providers.

Bolanle Olapeju, Ting Dong, Michael Soh, Omar Mushtaq, Hanna Chang, David Riegleman, Paul A Hemmer, Stephen D Schwab, Patrice Shanahan, Kimberly Johnson-Boua and 1 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Coaching the Caregivers: A Burnout Intervention for Military Physicians and Medical Students.International journal of environmental research and public health · 2026
    Observational
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Bolanle OlapejuDepartment of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
Ting DongCenter for Health Professions Education, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
Michael SohCenter for Health Professions Education, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
Omar MushtaqDepartment of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
Hanna Chang375th Medical Group, Flight Operational Medicine Clinic, Scott Air Force Base, IL, United States.
David RieglemanSAUSHEC Medical Education Department, Brooke Army Medical Center, Fort Sam Houston, TX, United States.
Paul A HemmerDepartment of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
Stephen D SchwabAlvarez College of Business, University of Texas at San Antonio, San Antonio, TX, United States.
Patrice ShanahanDepartment of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
Kimberly Johnson-BouaDepartment of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.
Steven J DurningCenter for Health Professions Education, Uniformed Services University of the Health Sciences, Bethesda, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Health provider burnout is highly prevalent (28-51%) in the US and may contribute to a projected national health provider shortage by 2030. The Socioecological Model (SEM) is a proven conceptual framework used to identify influencing factors and design relevant solutions to improve health outcomes across multiple ecological levels. This study applied the SEM to identify modifiable drivers and solutions of burnout across multiple levels among US Military health providers. Methods: We conducted a cross-sectional mixed methods study using an online survey ( Results: About two-thirds (63%) of surveyed providers self-reported burnout. Individual-level factors significantly associated with burnout included a positive coping style (AOR = 0.21; 95% CI: 0.08-0.56), perceived control over workload (AOR = 0.17; 95% CI: 0.04-0.66), and satisfaction with the current job (AOR = 0.11; 95% CI: 0.03-0.39). At the organizational level, providers described as overworked (AOR = 10.58; 95% CI: 3.30-33.91) or in hectic or chaotic primary work areas (AOR = 7.54; 95% CI: 2.33-24.38) had higher rates of burnout. At the military level, providers who were happy with their career path and promotion plan (AOR = 0.16; 95% CI: 0.06-0.44) reported lower rates of burnout. The organizational level had the highest cumulative impact of modifiable factors on burnout (AOR: 0.15; 95% CI: 0.06, 0.36). Qualitative interviews corroborated survey findings and highlighted the individual level manifestations of burnout, the role of interpersonal support as mitigators of burnout and the complexity of governmental and military policies impacting provider wellness. Discussion: Identified factors influencing burnout at various levels may inform potential data-driven interventions to ensure a functional and vibrant US Military health. Data-driven strategies may include opportunities to balance work demands with resources and ability to cope as well as improve positive coping skills, attitudes and experiences related to work.

Indexed as

Burnout, ProfessionalHealth PersonnelMilitary PersonnelAdultCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and QuestionnairesUnited Statesburnouthealth providermilitarysocioecological modelwellbeing

Identifiers

PMID39624411
PMCPMC11609726

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.